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nvidia/
omnivinci

Name: omnivinci
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OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

Introduction

OmniVinci is an NVIDIA research project focused on exploring omni-modal LLMs that can not only see and read but also listen, speak, and reason.

We are among the best omni-modality understanding models. Check out our performance on some of the most popular omni-modality, audio, and vision benchmarks:

Quickstart

Below, we provide simple examples to show how to use our model with Transformers.

Environment Setup

Download and navigate to the HuggingFace repository:

huggingface-cli download nvidia/omnivinci --local-dir ./omnivinci --local-dir-use-symlinks False
cd ./omnivinci

Install Python environment (based on NVILA codebase):

bash ./environment_setup.sh omnivinci

🤗 Transformers Usage

Video (with Audio) Inference Example

python

from transformers import AutoProcessor, AutoModel, AutoConfig,AutoModelForCausalLM
import torch
import os

# default: Load the model on the available device(s)
model_path = "./"
video_path = "xxx.mp4"
generation_kwargs = {"max_new_tokens": 1024, "max_length": 99999999}
load_audio_in_video = True
num_video_frames = 128
audio_length = "max_3600"

config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)

model = AutoModel.from_pretrained(model_path,
                                  trust_remote_code=True,
                                  torch_dtype="torch.float16",
                                  device_map="auto")

processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
generation_config = model.default_generation_config
generation_config.update(**generation_kwargs)

model.config.load_audio_in_video = load_audio_in_video
processor.config.load_audio_in_video = load_audio_in_video
if num_video_frames > 0:
    model.config.num_video_frames = num_video_frames
    processor.config.num_video_frames = num_video_frames
if audio_length != -1:
    model.config.audio_chunk_length = audio_length
    processor.config.audio_chunk_length = audio_length


conversation = [{
        "role": "user",
        "content": [
            {"type": "video", "video":video_path},
            {"type": "text", "text": "Assess the video, followed by a detailed description of its video and audio contents."}
        ]
}]
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)

inputs = processor([text])

output_ids = model.generate(
    input_ids=inputs.input_ids,
    media=getattr(inputs, 'media', None),
    media_config=getattr(inputs, 'media_config', None),
    generation_config=generation_config,
)
print(processor.tokenizer.batch_decode(output_ids, skip_special_tokens=True))

For audio and image inference examples, please refer to example_mini_audio.py and example_mini_image.py.

License / Terms of Use

The model is released under the NVIDIA OneWay Noncommercial License.

Citation

Please consider to cite our paper and this framework, if they are helpful in your research.

bibtex

@article{ye2025omnivinci,
  title={OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM},
  author={Ye, Hanrong and Yang, Chao-Han Huck and Goel, Arushi and Huang, Wei and Zhu, Ligeng and Su, Yuanhang and Lin, Sean and Cheng, An-Chieh and Wan, Zhen and Tian, Jinchuan and others},
  journal={arXiv preprint arXiv:2510.15870},
  year={2025}
}